Fake reviews detection based on LDA

Jia, Shaohua; Zhang, Xianguo; Wang, Xinyue; Yang, Liu

doi:10.1109/infoman.2018.8392850

Cited by 32 publications

(8 citation statements)

References 5 publications

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“…More specifically, the average accuracy of 86% in the one-domain setting dropped to a range of 52% to 64% in a cross-domain setting, where a data set is kept for testing and the rest are used for training. LDA was also used by Jia et al (2018) along with term frequency and word2vec (Mikolov et al 2013) for the feature extraction step in a supervised approach to distinguish between fake and non-fake hotel and restaurant reviews. These different features types were examined both separately and in combination, while three classifiers were trained, namely logistic regression, SVM, and multilayer perceptron (MLP) (Rumelhart and McClelland 1987).…”

Section: Automated Text-based Deception Detectionmentioning

confidence: 99%

Deception detection in text and its relation to the cultural dimension of individualism/collectivism

et al. 2021

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Automatic deception detection is a crucial task that has many applications both in direct physical and in computer-mediated human communication. Our focus is on automatic deception detection in text across cultures. In this context, we view culture through the prism of the individualism/collectivism dimension, and we approximate culture by using country as a proxy. Having as a starting point recent conclusions drawn from the social psychology discipline, we explore if differences in the usage of specific linguistic features of deception across cultures can be confirmed and attributed to cultural norms in respect to the individualism/collectivism divide. In addition, we investigate if a universal feature set for cross-cultural text deception detection tasks exists. We evaluate the predictive power of different feature sets and approaches. We create culture/language-aware classifiers by experimenting with a wide range of n-gram features from several levels of linguistic analysis, namely phonology, morphology and syntax, other linguistic cues like word and phoneme counts, pronouns use, etc., and token embeddings. We conducted our experiments over eleven data sets from five languages (English, Dutch, Russian, Spanish, and Romanian), from six countries (United States of America, Belgium, India, Russia, Mexico, and Romania), and we applied two classification methods, namely logistic regression and fine-tuned BERT models. The results showed that the undertaken task is fairly complex and demanding. Furthermore, there are indications that some linguistic cues of deception have cultural origins and are consistent in the context of diverse domains and data set settings for the same language. This is more evident for the usage of pronouns and the expression of sentiment in deceptive language. The results of this work show that the automatic deception detection across cultures and languages cannot be handled in unified manners and that such approaches should be augmented with knowledge about cultural differences and the domains of interest.

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Section: Automated Text-based Deception Detectionmentioning

confidence: 99%

Deception detection in text and its relation to the cultural dimension of individualism/collectivism

et al. 2021

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“…In supervised learning, a function is used to map the input to the output depending on examples of related input-output pair. Supervised learning techniques that have been used for spam review detection so far are; Rule based classification [5,10], Unified model [2], Logistic Regression [4,11,12], Knearest neighbor (KNN) [4], Random Forest [4,[13][14][15], Decision Trees [16,17], Gradient Decent [4,10], Genetic Algorithm [18], Conceptual Model [19], Time Series [20], Neural Network [21], Deep Neural Network [22], Multinomial Naïve Bayes [9,11,13], N-Gram [13], Hybrid Learning Approach (Active and supervised learning) [23], RNN, CNN [24], and Multilayer Perceptron Model (MLP) [4,24], Unsupervised learning is a category of machine learning that work on the unlabeled datasets. Many unsupervised learning techniques have been used in spam detection which are: Natural Language Processing [6,9][58] Markov Network [25], Neural Auto-encoder Decision Forest [16]¸ and PU Learning [26].…”

Section: Figure 1 Types Of Spammentioning

confidence: 99%

Spam Review Detection:A Systematic Literature Review

Farooq¹

2020

Preprint

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In this era of technology, people rely on online posted reviews before buying any product. These reviews are very important for both the consumers and people. Consumers and people use this information for decision making while buying products or investing money in any product. This has inclined the spammers to generate spam or fake reviews so that they can recommend their products and beat the competitors. Spammers have developed many systems to generate the bulk of spam reviews within hours. Many techniques, strategies have been designed and recommended to resolve the issue of spam reviews. In this paper, a complete review of existing techniques and strategies for detecting spam review is discussed. Apart from reviewing the state-of-the-art research studies on spam review detection, a taxonomy on techniques of machine learning for spam review detection has been proposed. Moreover, its focus on research gaps and future recommendations for spam review identification.

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“…Word2vec, Doc2vec) (Krishnamurthy et al, 2018;Yilmaz and Durahim, 2018). A recent study by Jia et al (2018) explored the application of linguistic features to distinguish between fake and nonfake reviews. They used Yelp filter dataset in their study and applied Term Frequency, Word2vec, and Latent Topic Distribution for data representation.…”

Section: Related Workmentioning

confidence: 99%

Sentiment and Emotion Based Text Representation for Fake Reviews Detection

Melleng¹,

Jurek-Loughrey²,

Deepak³

2019

Proceedings - Natural Language Processing in a Deep Learning World

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Fake reviews are increasingly prevalent across the Internet. They can be unethical and harmful. They can affect businesses and mislead customers. As opinions on the Web are increasingly relied on, the detection of fake reviews has become more critical. In this study we explore the effectiveness of sentiment and emotions based representations for the task of building machine learning models for fake reviews detection. The experiment performed with three real-world datasets demonstrate that improved data representation can be achieved by combining sentiment and emotion extraction methods, as well as by performing sentiment and emotion analysis on a part-by-part basis by segmenting the reviews.

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Fake reviews detection based on LDA

Cited by 32 publications

References 5 publications

Deception detection in text and its relation to the cultural dimension of individualism/collectivism

Deception detection in text and its relation to the cultural dimension of individualism/collectivism

Spam Review Detection:A Systematic Literature Review

Sentiment and Emotion Based Text Representation for Fake Reviews Detection

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